---
res:
  bibo_abstract:
  - Clustering is an important field in data mining that aims to reveal hidden patterns
    in data sets. It is widely popular in marketing or medical applications and used
    to identify groups of similar objects. Clustering possibly unbounded and evolving
    data streams is of particular interest due to the widespread deployment of large
    and fast data sources such as sensors. The vast majority of stream clustering
    algorithms employ a two-phase approach where the stream is first summarized in
    an online phase. Upon request, an offline phase reclusters the aggregations into
    the final clusters. In this setup, the online component will idle and wait for
    the next observation in times where the stream is slow. This paper proposes a
    new stream clustering algorithm called evoStream which performs evolutionary optimization
    in the idle times of the online phase to incrementally build and refine the final
    clusters. Since the online phase would idle otherwise, our approach does not reduce
    the processing speed while effectively removing the computational overhead of
    the offline phase. In extensive experiments on real data streams we show that
    the proposed algorithm allows to output clusters of high quality at any time within
    the stream without the need for additional computational resources.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Matthias
      foaf_name: Carnein, Matthias
      foaf_surname: Carnein
  - foaf_Person:
      foaf_givenName: Heike
      foaf_name: Trautmann, Heike
      foaf_surname: Trautmann
      foaf_workInfoHomepage: http://www.librecat.org/personId=100740
    orcid: 0000-0002-9788-8282
  bibo_doi: 10.1016/j.bdr.2018.05.005
  bibo_volume: 14
  dct_date: 2018^xs_gYear
  dct_language: eng
  dct_title: evoStream — Evolutionary Stream Clustering Utilizing Idle Times@
...